Kubeflow

End-to-end ML pipelines on Kubernetes — training, tuning, serving.

Maturity level: L3 — Can Deploy

Six perspectives on Kubeflow

Roadmap

Advanced MLOps — after K8s, Helm, and MLflow.

Architecture

Platform layer for ML pipelines on Kubernetes.

Company

Platform engineering and senior MLOps at scale.

Projects

Full pipeline on Kubeflow Pipelines.

Interview

ML platform design.

Career

AI platform engineer path.

What & Why

What: Kubernetes-native platform for ML workflows, pipelines, and Katib hyperparameter tuning.

Why: Large teams standardize on Kubeflow for multi-tenant ML platforms on K8s.

Build this

Kubeflow pipeline from data ingest → train → evaluate → deploy via KServe.

Production reality

  • ! Complex upgrades
  • ! Resource contention
  • ! Steep learning curve

Interview preparation

  • Kubeflow vs MLflow
  • When is Kubeflow worth the complexity?

Connected skills

KubernetesMLflowKServeHelm

Explore Kubeflow in the interactive universe or train with live cohorts.

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